s2fieldsofstudy: Biology Medicine

text:
A comparative mRNA- and miRNA transcriptomics reveals novel molecular signatures associated with metastatic prostate cancers

Background: Prostate cancer (PC) is a fatally aggressive urogenital cancer killing millions of men, globally. Thus, this study aims to identify key miRNAs, target genes, and drug targets associated with prostate cancer metastasis. Methods: The miRNA and mRNA expression datasets of 148 prostate tissue biopsies (39 tumours and 109 normal tissues), were analysed by differential gene expression analysis, protein interactome mapping, biological pathway analysis, miRNA-mRNA networking, drug target analysis, and survival curve analysis. Results: The dysregulated expression of 53 miRNAs and their 250 target genes involved in Hedgehog, ErbB, and cAMP signalling pathways connected to cell growth, migration, and proliferation of prostate cancer cells was detected. The subsequent miRNA-mRNA network and expression status analysis have helped us in narrowing down their number to 3 hub miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p, and hsa-miR-582-5p) and 9 hub genes (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1, and FGF2). Further investigations with different systems biology methods have prioritized NR3C1, ABHD2, and GSK3B as potential genes involved in prostate cancer metastasis owing to their high mutation load and expression status. Interestingly, down regulation of NR3C1 seems to improve the prostate cancer patient survival rate beyond 150 months. The NR3C1, ABHD2, and GSK3B genes are predicted to be targeted by hsa-miR-582-5p, besides some antibodies, PROTACs and inhibitory molecules. Conclusion: This study identified key miRNAs (miR-548c-3p and miR-582-5p) and target genes (NR3C1, ABHD2, and GSK3B) as potential biomarkers for metastatic prostate cancers from large-scale gene expression data using systems biology approaches.

Introduction
Prostate cancer is a urogenital cancer that accounts for 15% of all cancers in men globally (Group, UCSW, 2014;Van Gool and Pearson, 2014). PC is initially asymptomatic (Ratner, 2021), but painful urination, blood in the urine, or pelvic discomfort symptoms commonly arise as the disease progresses over the months and years (Screening and Board, 2002). The known risk factors for PC include increasing age, African-American ethnicity, fat rich diet, positive family history, and obesity (Bostwick et al., 2004). Early clinical testing and an accurate diagnosis of PC are critical in determining treatment options (Greenlee et al., 2001). The most common biomarker used to detect PC is prostate-specific antigen (PSA) test in the blood. However, it fails to distinguish indolent or aggressive cancer stages (Cancer, IAFRO, 2003). Besides this, increased age or an enlarged or inflamed prostate are the other factors that can also elevate serum PSA leading to misdiagnosis or unnecessary and costly treatment. This warrants the need to identify robust biomarkers that are not only specific and sensitive but also improve the overall accurate diagnosis and prognosis of prostate cancer.
Prostate cancer is estimated to have 58% heritability, highest among all major cancers (Lachance et al., 2018). Family-based linkage studies have identified a series of genes responsible for hereditary PC such as HPC1 (Carter et al., 1992;Cooney et al., 1997;Berry et al., 2000a), PCAP (Neuhausen et al., 1999;Berry et al., 2000a;Xu et al., 2001), HPCX (Schleutker et al., 2000), CAPB (Berry et al., 2000a;Xu et al., 2001), HPC20 (Berry et al., 2000b), HOXB13 (Breyer et al., 2012;Xu et al., 2013) etc. Over the last decades, Genome wide associated studies (GWAS) have identified about 160 common risk loci in PC, suggesting a polygenic model of PC (Farashi et al., 2019). Most of these risk loci genes are involved in the cell cycle or DNA repair (ATM, TERT, MYC, and MDM2), inflammatory response (IL8RB), and metabolism (JAZF1 and HNFB). However, underrepresentation of ethnic diversity necessitates us to look for universally applicable genetic susceptibility biomarkers for PC. Moreover, the molecular basis of PC could not be fully explained by candidate genetic variants alone, but through the global gene expression alterations. Some studies have confirmed the potential contribution of dysregulated gene expression changes in either blood (Wang et al., 1979;Papsidero et al., 1980;Kuriyama et al., 1981) or prostate biopsies (Ragde et al., 1988), and few were correlated with the survival rate of PC patients (Greenlee et al., 2001). These genes were mainly involved in diverse cellular functions like protein kinase binding, enzyme binding, cell activation and proliferation, Wnt regulation mechanisms, wound healing, apoptosis, etc. (Verras and Sun, 2006;Kypta and Waxman, 2012). However, the specific molecular regulators which control the gene expression changes have not been well characterized.
Gene expression is controlled at both post transcription and translation levels. MicroRNAs (miRNAs) are 18-22 bp long, and known to regulate more than 50% of protein-coding genes at post transcriptional level (Bartel, 2009). Few miRNAs have been identified as potential diagnostic biomarkers of tumour development and metastasis (Nikitina et al., 2012;Ilic et al., 2013). PC associated microRNAs in serum have the potential to successfully distinguish cancer and healthy controls. The aggressive PC is known to show overexpression of oncogenic miRNAs; miR-21, miR-125b, miR-221, and miR-222 (Sun et al., 2009). The miR-21 is overexpressed in PC and contribute to tumour growth (Nikitina et al., 2012) by knocking down PTEN and other tumour suppressor genes (Meng et al., 2007). The miR-200 family is regarded as key regulatory molecule in PC due to their down regulated expression tumor suppressive function and regulatory role in initiation and migration of prostate tumors (Feng et al., 2014). Furthermore, these 5 miRNAs when combined with routine PSA test were shown to improve the PC diagnosis (Siegel et al., 2020). However, influence of miRNAs on differentially expressed genes in prostate tissues is not explored in detail till now.
Owing to the sparse data, this study aimed to expand our current understanding of PC pathogenesis by exploring miRNA-mRNA interactions in prostate gland tissues. By involving a series of comprehensive bioinformatics approaches, this study has identified several gene-network clusters involved in cell communication, inflammation, proliferation, and differentiation processes which, are dysregulated in prostate cancers. Our findings provide a novel insight into understanding the mechanisms of PC, besides uncovering genetic markers with potential for disease diagnosis and therapeutic modulation.

Methodology
The workflow of current work is presented in Figure 1.

Collection of prostate cancer mRNA and microRNA expression datasets
The NCBI-GEO and EMBL-EBI Array express databases were initially searched for PC gene expression datasets using keywords like "Prostate Cancer, mRNA, and miRNA." From the output, one mRNA expression dataset, i.e., GSE6919, and one miRNA expression dataset, i.e., GSE21036, have been selected based on type and number of samples, data quality and expression array method used. The first dataset (GSE6919) consists of the gene expression data of 106 tissue samples (including 25 metastatic prostate tumours and 81 normal prostate tissues) generated on GPL8300 platform (Affymetrix human genome U95 version 2 array). The second dataset, GSE21036, consists of the expression data of miRNAs of 42 Samples (including 14 metastatic prostate tumours and 28 normal prostate tissues) generated on the GPL8227 platform (Agilent-019118 Human miRNA Microarray 2.0 G4470B (miRNA ID version).

Detection of differentially expressed genes and microRNAs in prostate cancer tissues
We performed differential expression analysis of miRNA and RNA profiles to find the prostate cancer specific dysregulations. We utilized GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) webtool of NCBI to detect differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in the test datasets. The GEO2R webtool conducts comparative analysis on microarray expression data sets utilizing the GEOquery and limma R packages available in Bioconductor software. The genes or miRNAs showing 1.5-fold changes (FC) at adj. p-values of <0.01 were identified. The expression pattern of these miRNAs and mRNAs were graphically represented in the form of volcano and median mean difference plots.

Identification of potential target genes for DEMs
In the initial phase, miRNA IDs of the DEMs were used to search for their potential target genes in MiRDB webserver (http://mirdb.org/mirdb/mining.html). The target genes showing <70 prediction scores and miRNAs with >2000 predicted targets in the genome were excluded from the output data. Then, keeping the miRDB data as reference, the inverse correlation analysis between miRNA-

FIGURE 1
Infographics of PC mRNA-miRNA analysis.
Frontiers in Genetics frontiersin.org 03 mRNA expression statuses was performed to screen the miRNAtarget gene pairs. The mathematical formula adopted for inversecorrelation of miRNA-mRNA levels is given below (Bima et al., 2022).
In the formula, "n" represents the correlation coefficient, "X" represents DEGs and "Y" represents DEMs.

GO annotation of microRNA target genes
The miRNA target genes identified from the inverse correlation analysis were further explored with KEGG (Kyoto Encyclopaedia of Genes and Genomes, http://www.genome.jp/kegg/) pathway analysis using ClueGO v2.5., a cytoscape plug-in. KEGG links the genes to functional pathways, with a p-value of <0.05 as an enrichment cut-off measure (Kanehisa and Goto, 2000). ClueGO facilitates the visualization of functionally related genes as a clustered network and overview chart. The statistical test used for the enrichment was based on two-sided hypergeometric option with a Bonferroni step down p-value correction and functional leading group term based on their Kappa score (≥ 0.3).

Construction of microRNA-target gene transcriptome network
The prostate cancer genes and miRNA functional interactome network was constructed using miRNET webserver (http://www.mirnet.ca). The miRbase ID and Entrez/Ensembl gene ID were given as input options for miRNAs and DEGs, respectively. The output of this tool is a transcription network along with different network parameters like centrality, betweenness, shortest path etc. The constructed network was visualized through Cytoscape 3.7.1 software. From the network hubs -genes and miRNAswere selected based on their highest centrality scores (Thul and Lindskog, 2018). Furthermore, the hypergeometric algorithm (option available in functional explorer plugin) was used to identify all KEGG pathways in which the hub genes were involved.

Functional assessment of microRNA target genes
In this step, the hub genes identified from miRNA-target gene pairs were further explored to identify their transcription factor (TF) motifs, and also to assess their drug tractability, expression status and mutational load.

Construction of microRNA-Target genetranscription factor network
The miRNA-target gene pairs from the network analysis were further explored in iRegulon plugin of Cytoscape to enrich their TF motifs (Janky et al., 2014). The motif prediction parameters were as follows, minimum orthologous gene identity of ≥0.05, the maximal false discovery rate (FDR) on motif similarity of ≤0.001, and normalized enrichment score (NES) of >3. Finally, the miRNA-mRNA-TF regulatory network was constructed utilizing Cytoscape 3.7.1.

Therapeutic potential of hub genes
The hub genes from miRNA-target gene network were explored in open target platform (http://www.targetvalidation. org). The input option for this tool is query gene ID and the output includes phenotype association characteristics (p = <0.05), predicted tractability (small molecule inhibitors and antibodies) and known drug information (target diseases, mode of action, clinical trials etc.) about the query gene.

Mutational load of hub genes in prostate cancers
All the query hub genes of miRNA-target network were explored in cBioPortal (http://www.cbioportal.org/) to assess their mutational load in prostate cancer tissues. Upon providing the gene ID and cancer type as an input data, oncoprint option available in this webserver visualises the genetic alterations of the queried genes in the form of heatmaps with z-score values. Additionally, the mutation pattern of hub gene pairs in prostate cancers is calculated with Log2 Odds Ratio (OR).

Expression analysis of hub genes in prostate cancers
The expression status of query hub genes in normal and cancer tissues, and their disease-free survival curve correlation analysis was performed in GEPIA2 (http://gepia2.cancer-pku. cn/) webserver. The genes showing |log2 fold change| with ≥1 were considered as significant at the p-value of <0.01. Disease-free correlation of hub gene expression status between PC and normal tissues was determined using a log rank test with p < 0.05.

Immunohistochemistry analysis
The expression status of hub genes across cell lines and tissues was estimated with Human protein atlas (https://www. proteinatlas.org/) database. This database intake the query gene or protein name and provides the immunocytochemistry/ immunofluorescence (ICC-IF) information about the candidate protein's subcellular location and expression (protein-transcripts per million, pTPM). pTPM, is a normalization method used for comparing the gene expression levels in the same tissue. We also analysed the Frontiers in Genetics frontiersin.org 04

FIGURE 2
Distribution of differentially expressed genes and miRNAs. Volcano plots of (A) GSE6919 and (B) GSE21036 datasets showing the transcript expression profiles mRNA and miRNA respectively, of metastatic prostate cancer and normal prostate tissues. Red and blue dots correspond to the up and down regulated genes, respectively, showing more than 1.5-fold expression difference (p < 0.05). (C) Wesker Box plot of inverse co-related miRNA and their target DEGs (D). DEGs and miRNA target gene filtration method.
Frontiers in Genetics frontiersin.org 05 immunohistochemistry data to enquire the tissue expression status of the query gene. Here the intensity of primary antibody staining between normal and tumour tissues was classified as negative, weak, moderate, or strong based on the detector grain settings used for image acquisition in combination with the visual appearance of the image.

Identification of differentially expressed target genes and microRNAs
The analysis of GSE6919 dataset has identified the expression of 9,153 genes corresponding to 12,558 probes. A total of 709 genes were found to be differentially expressed (log 2 ≥ 1.5 folds), between metastatic prostate tissues compared to normal prostate tissues. Of these DEGs, 290 (40.90%) were upregulated and 419 (59.10%) were down regulated (p < 0.05). Simultaneously, a total of 373 miRNAs corresponding to 373 probes were detected from the analysis of GES21036 dataset. Metastatic prostate tumours have demonstrated the dysregulated expression (log 2 ≥ 1.5 folds) of 65 miRNAs, including 28 upregulated and 37 downregulated when compared to normal prostate tissues (p < 0.05) ( Figures  2A,B). The details of top 4 genes and miRNAs are listed in Table 1.

Mapping of microRNA target genes
The miRDB webserver predicted that, 53 miRNAs (of the 65 differentially expressed miRNAs) modulate the expression of 27,541 target genes at a cut off score of <70.

Pathway enrichment of microRNA target genes
The main KEGG pathways enriched for 250 targets genes (125 up and 125 downregulated genes, each) of 53 miRNAs at the p-value threshold of <0.05 are listed in Table 3. The downregulated genes were enriched in prostate cancer, cAMP signalling, signalling pathways regulating pluripotency of stem cells, Th1 and Th2 cell differentiation, and cGMP-PKG signalling pathways. The upregulated genes were enriched in Hedgehog signalling, ErbB signalling, and T-cell receptor signalling pathways.

Systems biology validation of prostate cancer hub genes
We performed the functional validation of the 9 hub genes (with inverse regulated miRNAs mentioned above) in the prostate cancers, using different systems biology approaches.

Identification of TF and microRNA target gene network regulators
The enrichment findings of TF motifs for nine hub target genes and six miRNAs have predicted that the transcription factors like RARA, CRX, ZNF524, APEX2, and MYBL1 interacts with 6 hub genes, like ABHD2, DICER1, GSK3B, Frontiers in Genetics frontiersin.org 08 NACC2, NFIB, and NR3C1 (with network enrichment score of >3.5) (Table 4). Although, the miRNAs were not directly connected to TF motifs, but three miRNAs were predicted to regulate the network of TF-target gene network. For example, hsa-mir-548c-3p interacts with NACC2 and NR3C1, hsa-mir-455-3p interacts with NFIB, and hsa-mir-582-5p interacts with ABHD2 and DICER1 ( Figure 3C).

Molecular tractability potential of hub genes
The nine hub genes (9/32; 28%) selected from the topological analysis demonstrated the genotype-phenotype association score of >0.002 in the Open Target Validation Platform analysis. Tractability information for all 9 queried genes was available, with FGF2, GSK3B, and NR3C1 genes were tractable by small molecules, ABHD2, FGF2, and GSK3B genes were tractable by antibody molecules, ABHD2, FGF2, NR3C1, and GSK3B genes were targeted by Proteolysis Targeting Chimeras (PROTACs), while only FGF2 gene was tractable also by enzyme molecules (Table 5). Of note, the FGF2 acts as a molecular target for Muprafostat oligosaccharide agent, which is currently in phase 2 trial in prostate cancer. The GSK3B is targeted by lithium carbonate inhibitor which currently completed phase 1 trial in prostate cancer. The NR3C1 is targeted by several small molecule agonists like Dexamethasone, Prednisolone, and Prednisone which have currently completed phase 4 clinical trials (Supplementary Table S2).

Determination of mutation load of hub genes
The cBioPortal for Cancer Genomics analysis has confirmed that all the hub genes carry different types of alterations,   Table S3). Interestingly, in 5/9 genes (55.5%), i.e., ABHD2, DICER1, GSK3B, NR3C1, and NFIB mutations were localized to functional domains of their corresponding proteins, which suggest them to be highly critical genes for prostate cancers ( Figure 4B). The mutations in NFIB gene are also cooccur with NACC2, DCAF7 and FGF2 genes (OR is three; p < 0.001) ( Table 6).

Confirmation of expression status of hub genes in prostate cancer tissues
The GEPIA results of all the 9 hub genes, have showed that expression levels of ABHD2, DCAF7, and GSK3B were upregulated, FGF2, NACC2, and NR3C1 were downregulated in prostate adenocarcinomas (PRAD) than the normal prostate tissues by more than 1.5 folds (p < 0.01), reinforcing our Frontiers in Genetics frontiersin.org 11 understanding that these genes have important role in carcinogenesis ( Figure 5). On the other hand, DICER1 and FGFR1OP showed contradictory results to GEPIA analysis and the NFIB was non-significant.

The prognostic value of hub genes in prostate cancer patients
Kaplan-Meier survival curves showed that none of the 9 hub genes had any positive impact on improving overall survival of prostate cancer patients. However, the expression levels NFIB and NR3C1 were indicated to be of predictive for disease-free survival ( Figure 6A). The high expression level of NFIB could indicate poor survival rate in more than 50% of prostate cancer patients (p < 0.05). On the other hand, low expression level of NR3C1 could extend the disease-free survival rate in 45% of prostate cancer patients beyond 150 months (p < 0.05). In comparison, there were no obvious associations between the expression levels of ABHD2, FGF2, DCAF7, GSK3B, NACC2, DICER1, and FGFR1OP genes and survival rate in prostate cancer patients.

Immunocytochemistry/ immunofluorescence analysis
The protein expression of 6 hub genes in prostate cancer tissues are presented in Figure 6B. The results showed that ABHD2, DCAF7, and GSK3B were upregulated in cancer tissues compared with normal prostate tissues with high or medium intensity. However, the expression of DICER1, FGF2, NACC2, NFIB, FGFR1OP, and NR3C1 in PC tissues shown low intensity. The HPA indirect immunofluorescence analysis provides the FIGURE 5 RNA Expression levels in PC in comparison to normal tissues from GEPIA2. The box plot representation for ABHD2, FGF2, DCAF7, GSK3B, NACC2, and NFIB. The signature score is calculated by mean value of log2 (TPM +1). The |Log2FC| cut-off of the expression of proposed biomarker was 1. The significant cut-off p-value of the expression of proposed biomarker was 0.01. The red box indicates the tumor samples while grey colour indicates the normal tissues. Each dot represents one sample data in the category.
Frontiers in Genetics frontiersin.org subcellular localization of hub genes in different cell compartments in different human cancer cell lines. Our results showed that majority hub genes DCAF7, FGF2, GSK3B, NACC2, and NFIB were highly expressed and (nTPM >19) localised mainly in the nuclei of the cancer cells. While ABHD2, and DICER1 genes are mainly expressed (>18 nTPM) in the cytosol. Interestingly, the expression of NR3C1 is seen in both nuclear and cytosolic compartments (Supplementary Table S4; Figure 7). Our findings provide spatial information on protein expression patterns of hub genes and define the subcellular localization to cellular organelles and structures at a single cell level. In Frontiers in Genetics frontiersin.org 13 summary, our results indicated that protein expression levels of 9 hub genes are dysregulated in prostate cancers compared to normal prostate tissues.

Concordance analysis
Edward Venn diagram in Figure 8 represent a concordance analysis of nine hub genes and five miRNA using different paradigm system biology methods. In the six functional prediction tools, the genes ABDH2, NR3C1, and GSK3B are significantly enriched. Interestingly, TF-miRNA analysis revealed an indirect connection between hsa-miR-582-5 and hsa-miR-548c-3p and TF. These miRNAs have the potential to regulate ABDH2, NR3C1, and GSK3B.

Discussion
Prostate cancer has a high clinical heterogeneity, ranging from fairly indolent to a fatally aggressive form (Litwin and Tan, 2017). This necessitates the need to search for novel prostate tumour biomarkers that can improve the molecular diagnosis and clinical outcomes. Several investigations have identified unique molecular subtypes of primary prostate tumors as well as important genetic changes that contribute to metastasis   (Zhao et al., 2021) (De Schaetzen Van Brienen et al., 2021. But these studies differ from each other in sample type, study design, statistical measures, and systems biology methods used. Moreover, the data on the role of miRNAs in regulating gene expression changes in prostate cancer tissues is sparce (Doghish et al., 2021) (Gordanpour et al., 2012) (Watahiki et al., 2011). Therefore, this new study design combined the publicly available expression datasets from different prostate tissues and across different microarray platforms to precisely map the interactions of key genes and miRNA involved in prostate tumorigenesis.
By normalization of expression data and comparison to normal prostate tissues, we detected 250 differentially expressed genes (125 up-and 125 downregulated genes) in the prostate cancer tissues. Pathway enrichment of these key upregulated genes highlight their involvement in Hedgehog signalling and ErbB signalling pathways. The Hedgehog (Hh) signalling network plays an important role in metastatic human prostate tumours that expressed Hh ligands and Hh-target genes at higher levels (Karhadkar et al., 2004). Moreover, chemical inhibitors of Hh signalling have been demonstrated to suppress the growth of PC cell lines (Sanchez et al., 2005). ErbB-2 oncogene contributes to PC progression by regulating numerous signalling pathways, including AKT, ERK1/2 and STATs, by positively influencing cell survival, migration, and proliferation of cancer cells (Miller et al., 2019).
The downregulated genes were enriched in key prostate cancer pathway (KEGG:05,215), which regulates growth factor receptor

Frontiers in Genetics
frontiersin.org 14 mediated cytokine-cytokine interactions (Lee and Rhee, 2017). These growth factor receptors indirectly activate the RAS-MAPK signalling that regulates gene expression, cellular growth, and survival of prostate cancer cells (Rodríguez-Berriguete et al., 2012). Increased cell proliferation and resistance to apoptosis may be caused by abnormal MAPK signaling pathway (Dasgupta et al., 2020). cAMP serves as a intermediary messenger molecule in several signaling pathways such as cell growth and differentiation (Kim et al., 2005). In the context of PC, cAMP levels modulate the activity of androgen receptor (AR) signalling, which could negatively impact normal prostate development and function (Yan et al., 2016).
We also identified miRNA alterations targeting genes which are regulating the transition of normal prostate to aggressive cancerous cells. The hub gene is a gene with the highest degree of connectivity (at top 10%) in the key module and identifying the hub genes is shown to improve the molecular dissection of human diseases like obesity (Sabir et al., 2019), autoimmune diseases , myocardial infarctions (Mujalli et al., 2020), and cancers (Sahly et al., 2021). So, by constructing the miRNA-mRNA functional network of 250 target genes and 53 miRNAs, we mapped 32 hub target genes and six hub miRNAs showing high degree of network centrality parameter (>150).
The inverse correlation analysis of expression statuses of these target gene-miRNA pairs has narrowed down the total hub genes to 9 (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1, and FGF2) and hub miRNAs to 6 (hsa-miR-455-3p, hsa-miR-548c-3p, hsa-miR-582-5p, hsa-miR-671-5p, hsa-miR-125a-3p and hsa-miR-338-3p). Among the hub miRNAs exclusively deregulated in PC, only 3 miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p and hsa-miR-582-5p) were found to target 9 hub genes. The pathway analysis of these hub genes and miRNAs showed their involvement in different pathways connected to cell division or differentiation in response to extracellular signals, and a variety of cellular processes including gene expression, regulation of growth proteins, cell proliferation, oncogenic transformation, cell migration, and membrane trafficking in cancers (Kim et al., 2005). Thus, our findings broadly agree with previous studies, which highlighted differential regulation of key genes and miRNAs involved in the transformation of normal prostate tissue to prostate cancers (Gordanpour et al., 2012) (Zhao et al., 2021) (De Schaetzen Van Brienen et al., 2021. Both miRNAs and TFs regulate the gene expression at posttranscriptional and post-translation levels, respectively. Interestingly, miRNAs and TFs can regulate each other and also co-regulate a common target gene by forming a feed-forward loop (FFL) unit, which further forms gene regulatory networks (Qin et al., 2020). In this study, three miRNAs were predicted to co-regulate the network of TF-target gene network. For example, hsa-mir-548c-3p is shown to coregulate the interaction of NACC2 and NR3C1 with TF-ZNF524; hsa-mir-455-3p co-regulates the interaction of NFIB with TF-ZNF524, and hsa-mir-582-5p coregulate the interaction of ABHD2 and DICER1 with TF-ZNF524 and TF-CRX.
The comprehensive systems biology validation of 9 hub genes has revealed that downregulated miRNA like hsa-miR-582-5p is correlated with the elevated the expression of ABHD2 and GSK3B genes in prostate cancer tissues, and upregulated miRNA like hsa-miR-548c-3p correlates with the lower expression of NR3C1 (glucocorticoid receptor) gene. The hsa-miR-582-5p inhibits metastasis of prostate cancer by repressing multiple components of TGF-β signalling, resulting in the inactivation of TGF-β signalling (Gordanpour et al., 2012) (Huang et al., 2019). Of note, miR-548c-3p acts as regulator in many cancers by significantly affecting both ErbB and Hippo signalling pathways (Feng et al., 2019). Hence, both miRNAs can be potential biomarkers for metastatic prostate cancers. Open target analysis findings have predicted that, Proteolysis Targeting Chimeras (PROTACs) can target all ABHD2, GSK3B, and NR3C1 genes. Additionally, ABHD2 is targeted by antibody molecules, and GSK3B is targeted by lithium carbonate inhibitor which has currently completed phase 1 trial in prostate cancer. NR3C1 agonists like prednisone and dexamethasone are already under usage in lymphoid cancers and can be explored as potential repurposed drug for prostate cancers.
The GSK3B, a central component of PI3K/Akt survival pathway, determines the cancer cell survival through its effects on apoptosis. Expression status of GSK3B is positively correlated with tumor

FIGURE 8
Edwards' Venn diagram demonstrating overlapping of hub genes across six system biology analysis methods. The bar graph represents the number of genes in each prediction tool.
Frontiers in Genetics frontiersin.org 15 progression in multiple human malignancies (Li et al., 2015). Immunohistochemistry analysis of 499 PC surgical specimens showed higher levels of cytoplasmic (not nuclear) of GSK3B protein, in addition to its correlation with aggressive clinicopathological parameters such as late clinical stage, lymph node metastasis, extracapsular extension, and a high Gleason score, as well as a 2-fold reduction in recurrence-free survival with up to 12-years follow-up (Li et al., 2009). Since it is an androgen-regulated gene that is associated with the development of prostate cancer and its ability to resist chemotherapy, ABHD2 is a promising new target for prostate cancer screening and treatment. Gleason score, pathological node stage, low cancer-specific survival rates, and resistance to docetaxel-based chemotherapy have been linked to ABHD2 levels in prostate cancer specimens by immunohistochemical analysis. (Obinata et al., 2016). The NR3C1encodes a nuclear hormone receptor, acting as a transcription factor and modulate the gene expression across different tissues (Gu et al., 2017). Lower NR3C1 expression levels were found in cancer cells compared to normal tissues of breast (Mamoor, 2021). For lymphoid cancers, acute lymphoblastic leukemia, chronic lymphocytic leukemia and multiple myeloma, NR3C1 activation has been proven to be an effective treatment strategy. There are, however, several lines of evidence suggesting that GR activation has a strong effect on cancer cell behaviours such as invasion, apoptosis resistance and growth (Prekovic et al., 2021). Thus, it is imperative to use therapeutic interventions, whether agonists or antagonists to control the prostate carcinogenesis by regulating the expression of concerned candidate genes.
This study would like to sincerely clarify some limitations. First, we studied the role of miRNA and target expressions in metastatic prostate cancers. Thus, we do not generalise our findings neither blood samples nor primary tumors of prostate cancer patients. Second, we took microarray data of prostate tissue biopsies. Thus, mapping the key miRNAs or genes in body fluids like blood, urine and prostatic fluid is important to develop non-invasive biomarkers for prostate carcinogenesis. Third, the key hub genes and hub miRNAs identified in this study, need to be further validated by targeted approaches like real-time PCR, functional biology assays and on larger number of patient samples.
In conclusion, this study identified the inversely correlated dysregulation of 9 target genes (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1, and FGF2) and three key miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p and hsa-miR-582-5p). This study supports the robustness of computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks in identifying prostate cancer biomarkers from large-scale gene expression data. Tissue based prostate cancer markers can be correlated with cancer development, which might offer early clinical interventions and therapy. This study also provides the initial evidence for the future knowledge driven functional studies of these miRNAs and their gene targets in prostate cancer.

Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.


Transcriptome analysis identifies LGP2 as an MDA5-mediated signaling activator following spring viremia of carp virus infection in common carp (Cyprinus carpio L.)

The common carp (Cyprinus carpio L.) is an important farmed species worldwide. Mucosal-associated lymphoid tissues play an essential role in the fight against pathogen infection. Spring viremia of carp virus (SVCV) poses a serious threat to the common carp aquaculture industry. Understanding the molecular mechanisms driving mucosal immune responses to SVCV infection is critical. In this study, the mucosal tissues (gills, foregut and hindgut) were collected from normal and infected fishes for transcriptome analysis. A total of 932,378,600 clean reads were obtained, of which approximately 80% were successfully mapped to the common carp genome. 577, 1,054 and 1,014 differential expressed genes (DEGs) were identified in the gills, foregut and hindgut, respectively. A quantitative polymerase chain reaction assay indicated that the DEGs expression in the foregut following SVCV infection was consistent with the transcriptome results. Among them, two key genes of the retinoic acid-inducible gene I (RIG-I)-like receptor family, melanoma-differentiation-associated gene 5 (MDA5) and laboratory of genetics and physiology 2 (LGP2) (i.e., CcMDA5 and CcLGP2), underwent further analysis. Overexpression of CcMDA5 or CcLGP2 increased phosphorylation of TANK-binding kinase 1 and interferon regulatory factor 3 and the expression of interferon-1 (ifn-1), myxovirus resistance (mx), viperin and interferon-stimulated gene 15 (isg15), and inhibited SVCV replication in epithelioma papulosum cyprini cells. Furthermore, CcLGP2 significantly upregulated the CcMDA5-induced ifn-1 mRNA expression and the activation of the ifn-1 promoter. Finally, confocal microscopy and coimmunoprecipitation experiments revealed that CcLGP2 colocalizes and interacts with CcMDA5 via the C-terminal regulatory domain. This study provides essential gene resources for understanding the fish immune response to SVCV infection and sheds light on the potential role of fish LGP2 in the MDA5 regulation.

Introduction
Spring viremia of carp virus (SVCV) belongs to the Vesiculovirus genus of the Rhabdoviridae family (1). It is a linear, negative-sense, single-stranded RNA virus that is believed to be the cause of spring viremia of carp (2). Affected fish suffer from acute hemorrhagic illness and have a high mortality rate (3). Once infected, it is difficult to eradicate the virus from affected ponds (4). Currently, no specific medicine is approved for the treatment of SVCV-induced diseases, nevertheless, safe and effective vaccines are being developed. Hence, it is critical to explore the intricate mechanisms of hostvirus interaction.
The innate immune cells utilize pattern recognition receptors (PRRs) to sense invading viruses (5) (6). The retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) are the key intracellular PRRs that can detect viral RNA (7), including RIG-I, melanoma differentiation-associated protein 5 (MDA5) and laboratory of genetics and physiology 2 (LGP2) (8). Upon RNA virus recognition, RIG-I and MDA5 undergo conformational changes and then recruit and interact with a mitochondrial adaptor protein (MAVS) (9). MAVS aggregation transmits a signal to downstream signaling molecules, activating the TANK-binding kinase 1 (TBK1) and the inhibitor of nuclear factor kappa-B kinase epsilon (IKKϵ). These kinases, in turn, activate interferon regulatory factor 3 (IRF3) and nuclear factorkappa B (NF-kB), leading to the production of interferon (IFN) and proinflammatory cytokine (10).
LGP2 was initially identified as a negative regulator of RIG-I and MDA5 signaling. For instance, LGP2 interacts with RIG-I via its C-terminal regulatory domain (RD) to block RIG-I self-association and signaling (15). In contrast, LGP2-deficient mice have a positive regulatory role in RIG-I-and MDA5mediated antiviral responses (16). So far, RLR orthologs have been cloned and characterized from various fish species (17). However, the molecular mechanism of LGP2 regulation in RIG-I and MDA5 remains inconsistent in teleost. For instance, black carp LGP2 associates with RIG-I/MDA5 to activate IFN signaling and the host antiviral innate response (18). Nonetheless, grass carp LGP2 inhibits IFN-b promoter stimulator 1 and IRF3 activation via RIG-I and MDA5 (19). Hence, it is important to determine the role of fish LGP2 in RIG-I/MDA5-mediated antiviral response.
Common carp (Cyprinus carpio L.), an important freshwater aquaculture species worldwide, is facing disease outbreak problems from bacterial, parasitic, and viral pathogens (20,21). Particularly, SVCV poses a serious threat to common carp health (22,23). Fish mucosal tissues, such as gills and gut, act as a protective barrier where pathogens against pathogen invasion in the aquatic environment (24). Hence, in this study, RNA-seq technology was utilized to describe differentially expressed genes (DEGs) in the gills, foregut and hindgut of common carp challenged by SVCV. Our results demonstrated that SVCV infection elicited a strong immune response in the mucosal tissues. CcMDA5 and CcLGP2 were selected for further research among these DEGs. Their antiviral activities and regulatory mechanisms were specifically investigated. The study emphasized the fish mucosal immune response to SVCV and will provide new insights into the potential role of fish LGP2 in the MDA5 regulation.

Animal samples and viral challenge
Common carp (C. carpio L.) with a body weight of 150−200 g were obtained from a fish farm in Jinan and adapted for at least one week before being used in experiments. SVCV were kindly gifted from Yuguang Qin (Shandong Freshwater Fisheries Research Institute, China) and quantified by our laboratory according to previous research (25). The fishes were randomly divided into SVCV-infected and control groups. Carps in the infected group were intraperitoneally injected with 500 mL of SVCV (10 6 TCID 50 /mL), and the control group received an equal volume of Medium 199 (Gibco, Grand Island, NY, USA). Three fishes were randomly selected from the control and infected groups and immersed in a 100 mg/L-Tricaine Methane Sulfonate solution. Additionally, carp gills, foregut and hindgut samples were collected at 72 h post SVCV or Medium 199 injection. The samples were promptly frozen in liquid nitrogen and stored at -80°C until use.

RNA isolation, library construction and sequencing
Total RNA was isolated using TRNpure Total RNA Kit (Nobelab, Beijing, China) following the manufacturer's protocol. The quantity and purity of total RNA were determined using NanoDrop 2000 (Thermo Fisher, Waltham, MA, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA), respectively. RNA purification, reverse transcription, library construction and sequencing were performed at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer's instructions (Illumina, San Diego, CA, USA). The transcriptome library was prepared following the TruSeqTM RNA sample preparation Kit from Illumina (San Diego, CA, USA) using 1 mg of total RNA. The messenger RNA was isolated using oligo (dT) beads and fragmented by fragmentation buffer. The doublestranded complementary DNA (cDNA) was synthesized using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA) with random hexamer primers (Illumina, San Diego, CA, USA) and subjected to end-repair, phosphorylation, and 'A' base addition per Illumina's library construction protocol. Libraries were size selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose followed by PCR amplification including 15 PCR cycles, using Phusion DNA polymerase (NEB, Ipswich, MA, USA). After being quantified by TBS380, the paired-end RNA-seq sequencing library was sequenced with the Illumina NovaSeq 6000 sequencer (2 × 150 bp read length).

Identification and functional enrichment analysis of DEGs
Raw reads were first quality checked by Sickl (https://github. com/najoshi/sickle) and SeqPrep (https://github.com/jstjohn/ SeqPrep) to eliminate adaptor sequence, low-quality sequence and reads containing polyN. The trimmed reads were assessed by fastp v0.19.5, and the high-quality clean reads were aligned with the reference genome of C. carpio (GCF_000951615.1) using TopHat Version v2.1.1. The clean data were analyzed on the Majorbio Cloud Platform as previously described (26). Briefly, HISAT2 software was used to map the clean reads to the common carp genome. The mapped reads were assembled using StringTie software. To identify DEGs between the control and SVCV groups (three replicates in each group), TPM (transcripts per million) were used to assess the gene expression. DEGs analysis between two groups was done using DESeq2 software. A false discovery rate (FDR) < 0.05 and |log2 (Fold Change, FC)| ≥1 were used as the threshold for the selection of the DEGs. Lastly, functional-enrichment analysis including Gene Ontology (GO, http://www.geneontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http:// www.genome.jp/kegg/) were performed to identify DEGs that were significantly enriched in GO terms and metabolic pathways at P-adjust ≤ 0.05 when compared to the whole-transcriptome background. GO functional enrichment and KEGG pathway analysis were performed using Goatools (https://github.com/ tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu. cn/home.do).

Quantitative polymerase chain reaction
Total RNA was extracted from tissues and cells using TRNpure Total RNA Kit (Nobelab, Beijing, China), and 2 mg of total RNA was reverse-transcribed using ReScriptTM II RT SuperMix for qPCR (+gDNA Eraser) (Nobelab, Beijing, China) following the manufacturer's instructions. Subsequently, the gene expression was detected using 2 × SYBR qPCR Mix (Nobelab, Beijing, China) and performed on a LightCycler 96 instrument (Roche, Switzerland). The qPCR was performed as follows: 95°C for 120 s followed by 40 cycles of 95°C for 10 s and 60°C for 30 s. The relative expression of genes was analyzed using the 2 −DDCT method. The primers used in this study were listed in Table 1.

Cell culture and transfection
Epithelioma papulosum cyprini (EPC) cell line is derived from fathead minnow (Pimephales promelas), which is widely used in fish disease research. 293T cell line is a derivative of human embryonic kidney 293 cells commonly used to detect interactions between proteins. EPC and 293T cells were generously donated by Yuguang Qin (Shandong Freshwater Fisheries Research Institute, China) and Guangxun Meng (Institute Pasteur of Shanghai, Chinese Academy of Sciences, China), respectively. EPC cells were cultured in Medium 199 (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, Grand Island, NY, USA) and cultivated at 25°C without CO 2 . 293T cells were grown in Dulbecco's modified Eagle's medium (DMEM) (Gibco, Grand Island, NY, USA) supplemented with 10% FBS (Gibco, Grand Island, NY, USA), 100 U/mL penicillin and 100 mg/mL streptomycin (Life Technologies, Grand Island, NY, USA) and maintained at 37°C with 5% CO 2 . Target genes were transfected into 293T and EPC cells using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) and jetPRIME reagent (Polyplus, Illkirch, France), respectively.

Gene cloning and plasmid construction
Plasmid enhanced green fluorescent protein (pEGFP)-N1 plasmid was purchased from Clontech (Mountain View, CA, USA). The plasmids of pmCherry-N1, pFUGW-2FLAG and pCMV-HA were generously donated by Guangxun Meng. To construct the pEGFP-CcMDA5 and pEGFP-CcLGP2 fusion vector, the whole open reading frame of CcMDA5 (GenBank accession number: KM374815.1) and CcLGP2 (GenBank accession number: KM374816.1) were produced using PCR and digested with the corresponding restriction enzymes (HindIII/BamHI for CcMDA5 and KpnI/SmaI for CcLGP2). The digested sequence was ligated into pEGFP-N1 using T 4 DNA ligase (Life Technologies, Carlsbad, CA, USA). The other recombinant plasmids of CcMDA5 and CcLGP2 used in this study were cloned with the same method. The ifn-1 promoter luciferase reporter plasmid was constructed as described previously (27).

Confocal microscopy
EPC cells were seeded onto coverslips in a 24-well plate overnight before being transfected with EGFP-CcMDA5 and mCherry-CcLGP2 recombinant plasmids as depicted in the figures. The cells were rinsed with PBS and fixed with 4% PFA (paraformaldehyde) for 30 min at room temperature. Images were obtained using a laser scanning confocal microscope (Leica SP8, Wetzlar, Germany) after staining with 4',6-diamidino-2phenylindole (DAPI, Sigma Aldrich, St. Loius, MO, USA) for 10 min at room temperature.

Luciferase activity assays
293T cells in a 96-well plate were co-transfected with a luciferase reporter plasmid (ifn-1-Luc), pRL-TK Renilla luciferase plasmid, target gene recombinant plasmid and the control vectors. After 48 h of transfection, cells were lysed using Dual-Glo ® Reagent (Promega, Madison, WI, USA). Firefly luminescence was measured after 10 min of incubation at room temperature. Dual-Glo ® Stop & Glo ® Reagent (Promega, Madison, WI, USA) were further combined in an equivalent volume. Ten minutes later, the activity of Renilla luciferase was determined. The Firefly luciferase/Renilla luciferase ratio was calculated following the manufacturer's instructions.

Crystal violet staining
EPC cells were cultivated in a 24-well plate and transfected with 0.5 mg of CcMDA5/CcLGP2 recombinant plasmid or the empty vector. After transfection for 24 h, the cells were infected with SVCV (10 6 TCID 50 /mL, 30 mL) for twenty-four hours. Then the cells were washed with PBS, fixed with 4% PFA and dyed with 1% crystal violet for 30 min at room temperature.

Coimmunoprecipitation and immunoblot analysis
Immunoprecipitation and immunoblotting were performed as described previously (28). Briefly, 293T cells seeded in 6-well plates were collected after 48-h transfection with the indicated

Primer name Primer (5′!3′) Application
LGP2-GFP-F GGGGTACCATGGACTTCAGTCTTAGACC Plasmid LGP2-GFP-R CCCCCGGGTCAGTCGTTTAAGTTTAGGT Plasmid plasmids. The cells were washed with PBS and lysed using lysis buffer (50 mM Tris-HCl, 15 mM ethylenediamine tetraacetic acid with pH 8.0, 150 mM NaCl, 1 mM NaF, 1 mM Na 3 VO 4 , and 1% Nonidet P-40) with protease inhibitor cocktail (Roche, Switzerland) at 4°C. After being centrifugated for 10 min at 12,000 × g, the supernatant was collected and transferred into a fresh tube. The remaining supernatants were immunoprecipitated using anti-FLAG conjugated agarose (Sigma-Aldrich, St. Louis, MO, USA) at 4°C overnight with constant agitation. The immunoprecipitated proteins were collected on the following day. After washing four times with lysis buffer, the complex was resuspended with 2 × sodium dodecyl sulfate (SDS) loading buffer (as the IP sample) and heated at 95°C. Subsequently, the wholecell lysates and IP samples were separated by 10% SDSpolyacrylamide gel electrophoresis and transferred into a polyvinylidene fluoride membrane (Millipore, Bedford, MA, USA). The remaining steps of immunoblot were the same as previously stated (28).

Statistical analysis
Statistical analysis was performed using GraphPad Prism 7.0 software. All data were presented as mean ± standard deviations (SD). Comparisons in experiments between two groups were assessed by t-test and a one-way analysis of variance was used in more than two groups. P < 0.05 was considered statistically significant.

RNA Sequencing
A total of 18 samples (gills, foregut and hindgut tissues from the control and SVCV-infected group) were sequenced to screen for the critical genes involved in the immune response to SVCV infection. After Illumina sequencing, a total of 941,722,336 reads were identified. Furthermore, 932,378,600 clean reads were selected after quality control by filtering low-quality sequences. The benchmark for sequencing quality Q 30 > 90%. Among the 18 samples, more than 80% of the clean reads were successfully mapped to the reference genome ( Table 2). These results indicate that the transcriptome data were of high quality and the obtained unigenes were reliable and suitable for annotation analysis.

Identification of DEGs
To study the genetic variation in response to SVCV, DEGs were screened by comparing transcriptome data. As presented in the volcanic plots ( Figure 1A), a total of 2,645 DEGs were identified between the control and SVCV-infected groups. Among the 2,645 DEGs, 417 up-regulated and 160 downregulated genes were detected in the gills, 815 up-regulated and 239 down-regulated genes were found in the foregut and 701 up-regulated and 313 down-regulated genes were identified in the hindgut, respectively. The DEGs induced by SVCV in the gills, foregut and hindgut are depicted in the Venn diagram. As presented in Figures 1B, C, 217 up-regulated and 7 downregulated DEGs were common in the gills, foregut and hindgut.

GO and KEGG enrichment analyses of upregulated DEGs
GO and KEGG function enrichment analyses were used to better understand the biological roles of DEGs observed in the gills, foregut and hindgut. GO enrichment analysis revealed that the identified upregulated DEGs were significantly enriched in terms associated with innate immune response and host responses to the virus, such as defense response to the virus (biological process) and transferase activity, transferring pentosyl groups (molecular function) (Figure 2A). Furthermore, to determine the putative biochemical pathways of important upregulated DEGs, we identified the top 20 pathways using KEGG enrichment analysis. As depicted in Figure 2B, the upregulated DEGs were also significantly enriched in infectious diseases and pathways associated with innate immunity, such as Influenza A (22 genes), RIG-I-like signaling pathway (11 genes), Cytosolic DNA-sensing pathway (10 genes), PPAR (Peroxisome proliferator-activated receptors) signaling pathway (10 genes), Toll-like receptor signaling pathway (7 genes) and NF-kB signaling pathway (6 genes). The GO and KEGG function enrichment analyses were performed to analyze the respective identified upregulated DEGs in the gills, foregut and hindgut (Supplementary Figure 1). Furthermore, we analyzed the upregulated DEGs enriched in infectious diseases and pathways associated with innate immunity and constructed a heatmap. It was revealed that many RLR signaling pathway molecules, including LGP2, MDA5, IRF3 and IRF7, were significantly upregulated ( Figure 2C). RLR signaling pathway has been reported to play pivotal roles in the antiviral innate immune response to RNA virus. Considering the controversial roles of LGP2 regulating MDA5 in various viral infections, we selected CcMDA5 and CcLGP2 for further analysis.

The validation of DEGs
To verify the reliability of the transcriptome data, ten genes were randomly selected, and qPCR was performed in the foregut. These genes comprised tripartite motif-containing protein 21 (TRIM21), MDA5, LGP2, cyclic GMP-AMP synthase (cGAS), interferon-induced protein 44 (IFI44), rasrelated GTP-binding protein (RAS), golgi membrane protein 2 (GLOM2), heat shock factor protein (HSP), forkhead-box (FOX) and TLC domain-containing protein 4-B (TLCD4B). As depicted in Figure 3, the expression of TRIM21, MDA5, cGAS, LGP2, and IFI44 was increased and the expression of RAS, GLOM2, HSP, FOX and TLCD4B was decreased in the foregut following SVCV infection. Consequently, the confirmed expression profile of DEGs was consistent with obtained RNAseq analysis.

CcMDA5-mediated signaling inhibits SVCV infection
To describe the potential role of CcMDA5, we overexpressed CcMDA5 in EPC cells and assessed the expression of the downstream antiviral molecules. As illustrated in Figure 4A, Western blot results revealed that CcMDA5 overexpression induced phosphorylation of TBK1 (p-TBK1) and IRF3 (p-IRF3) in EPC cells. Additionally, overexpression of CcMDA5 caused a marked upregulation of interferon-1 (ifn-1), myxovirus resistance (mx), viperin and interferon-stimulated gene 15 (isg15) (324.25-fold, 72.07-fold, 129.99-fold and 84.15-fold, respectively), compared with the control cells ( Figure 4B). To identify the role of CcMDA5 in virus proliferation, the mRNA expression of the svcv-g gene and virus-induced cytopathic effects (CPEs) were explored. Compared with the control cells,  Validation of DEGs in the foregut. The mRNA levels of DEGs were normalized to the level of the S11 gene.
A B D C FIGURE 4 The effect of CcMDA5 in the antiviral immune responses. (A, B) Empty vector or GFP-tagged CcMDA5 plasmid was transfected into epithelioma papulosum cyprini (EPC) cells for 24 h. (A) Western blot was performed to test the phosphorylation of TBK1 and IRF3. b-Actin was used as an internal reference. (B) Total RNA was extracted to detect interferon-1 (ifn-1), myxovirus resistance (mx), viperin, and interferon-stimulated gene 15 (isg15) using qPCR. Data are represented as fold induction relative to the transcriptional level in control, which was set to 1. Mean ± SD (n = 3). (C, D) EPC cells were transfected with the above-mentioned plasmids. At 24 h post-transfection, the cells were stimulated with SVCV or Medium 199 and incubated at 25°C for 24 h. (C) Cell samples and supernatants were collected. The mRNA expression of the svcv-g gene was detected using qPCR. (D) The cell monolayers were stained with crystal violet, and CPEs were observed. The mock group represented the cells without treatment. Each experiment was conducted thrice. *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. CcMDA5 significantly decreased the transcriptional level of the svcv-g gene and CPEs (Figures 4C, D). These results revealed that CcMDA5 activates IFN signaling and inhibit virus proliferation.
CcLGP2 positively regulates CcMDA5 inducing ifn-1 expression Previous studies have revealed that teleost LGP2 exhibited different antiviral activities against viral infection. To describe the potential role of LGP2 in common carp, we overexpressed CcLGP2 in EPC cells. As illustrated in Figures 5A, B, the phosphorylation of TBK1 and IRF3 and the transcription of ifn-1, mx, viperin and isg15 were significantly enhanced after overexpression of CcLGP2. Additionally, CcLGP2 also reduced the replication of the svcv-g gene and CPEs (Figures 5C, D).
To further explore the effect of CcLGP2 on the CcMDA5mediated signaling pathway, qPCR and reporter assay were performed in EPC and 293T cells, respectively. As illustrated in Figure 6A, in comparison with the CcMDA5 group, the expression of ifn-1 was significantly up-regulated following cotransfection of CcLGP2 and CcMDA5. Additionally, CcLGP2 significantly enhanced CcMDA5-induced ifn-1 promoter activation in 293T cells ( Figure 6B). These results revealed that CcLGP2 acts as the positive regulator in the MDA5-mediated ifn-1 activation.

Association between CcLGP2 and CcMDA5
To clarify the regulatory mechanism between CcLGP2 and CcMDA5, confocal microscopy and coimmunoprecipitation experiments were conducted. It was observed that CcLGP2 colocalized with CcMDA5 in the EPC cells cytoplasm ( Figure 7A and Supplementary Figure 2). The CO-IP assay further verified their association, indicating an interaction between CcLGP2 and CcMDA5 ( Figure 7B). Additionally, we CcLGP2 enhances the IFN activity and inhibits viral replication. Empty vector or GFP-tagged CcLGP2 plasmid were transfected into EPC cells for 24 h. Further, the protein and RNA were extracted to detect downstream signaling molecules by Western blot (A) and qPCR (B). (C, D) EPC cells were transfected with the above-mentioned plasmids. Twenty-four hours post-transfection, the cells were infected with SVCV and incubated at 25°C for 24 h. (C) Cell samples and supernatants were collected, and the expression of svcv-g was detected using qPCR. (D) The cell monolayers were stained with crystal violet, and CPEs were observed. Each experiment was conducted thrice. ***P < 0.001, ****P < 0.0001 and ns indicates no significant difference.
investigated the domain of CcMDA5 that interacted with CcLGP2. As depicted in Figures 7C, D, different truncations of CcMDA5 were generated, and the CO-IP assay revealed that CcLGP2 was associated with the full-length of CcMDA5, albeit not with other truncations, suggesting that CcLGP2 bound with the C-terminal RD of CcMDA5. Overall, these observations indicate that CcLGP2 interacts with CcMDA5 via the RD.

Discussion
Teleost mucosal tissues serve as the first line of defense in innate immunity and play an essential role against pathogen infection (29,30). Previous studies have demonstrated that the microbial composition of mucosal tissues is changed, and the mucosal immune response is also activated during pathogen stimulation (31). Although studies have illustrated the changes in microbial composition following pathogen infection and environment changes (31,32), the antiviral mechanism of mucosal tissues remains elusive. Therefore, in this study, we selected carp mucosal immune tissues (gills, foregut and hindgut) for RNA-Seq analysis to explore the changes in the host immune system following SVCV infection. Following Illumina sequencing, 932,378,600 high-quality reads and 2,645 DEGs were identified.
KEGG analysis revealed that most of the co-upregulated DEGs were involved in pathogenic infection-related pathways, such as Influenza A, Cytosolic DNA-sensing pathway, RIG-Ilike signaling pathway, PPAR signaling pathway and Toll-like receptor signaling pathway. As reported in previous studies, these pathways play essential roles in antiviral immune response (33)(34)(35). In transcriptome profiling, there were several genes identified as significantly up-regulated genes, including RIG-I, MDA5, LGP2, cGAS, IRF3 and IRF7. cGAS is the major sensor for detecting DNA and RNA viruses (36). Mice with cGAS deficiency have a higher mortality rate and viral titer (37). In zebrafish, cGAS knockdown inhibits Edwardsiella tarda-induced production of immunoglobulin (Ig)Z in gills and skin (38). Therefore, cGAS plays a crucial part in mucosal immunity against SVCV in common carp. IRFs are a family of transcription factors, playing important and diverse roles in viral infections by activating IFNs (39). IRF3 and IRF7 are the most important regulators of type I IFN against viral infection (40,41). Hence, the initiation of transcription of IRF3 and IRF7 regulates IFN signaling against SVCV infection. RIG-I, MDA5 A B FIGURE 6 CcLGP2 activates the CcMDA5-inducing ifn-1. (A) Empty vector or FLAG-tagged CcMDA5 plasmid was co-transfected with GFP-tagged CcLGP2 plasmid into EPC cells for 24 h. Total RNA was extracted to detect ifn-1. Data are represented as fold induction relative to the transcriptional level in control, which was set to 1. (B) 293T cells were co-transfected with expression vectors for Firefly luciferase reporter gene with ifn-1 promoter, Renilla luciferase and target gene for 48 h. The relative activity of ifn-1 was the ratio of Firefly fluorescence to Renilla fluorescence. Mean ± SD (n = 3). Each experiment was conducted thrice. **P < 0.01, ***P < 0.001 and ****P < 0.0001. and LGP2, the members of RIG-I-like receptors, have been reported to be involved in the mucosal immune response against viral invasion (42). Additionally, our previous study has indicated a potential role of RIG-I in SVCV infection (26). Further studies are required to unravel the biological roles of MDA5 and LGP2 in SVCV infection. Previous studies have demonstrated that MDA5, together with RIG-I, are cytoplasmic sensors of double-stranded RNAs (43,44). MDA5 recruits the adaptor MAVS after interacting with viral RNA to initiate the production of IFN and antiviral molecules (45). The role of fish MDA5 in the induction of IFN has been well-elucidated during the last few decades (43,46,47). For instance, zebrafish MDA5 forms a complex with MAVS and then induces the IFN-I promoter activity (48). MDA5 from orange-spotted grouper activates the IFN promoter activity and interferon-stimulated response element (ISRE) activity and increases the transcription of IRF3, IRF7 and tumor necrosis factor receptor-associated factor 6 (TRAF6) (49). Consistent with these results, CcMDA5 overexpression not only significantly induced the phosphorylation of TBK1 and IRF3 but also promoted the expression of ifn-1 and ISGs. IFNs are a group of vital cytokines, which can induce a wide range of ISGs to limit virus replication and spread (50) (51). For instance, gibel carp IFNa remarkably inhibits cyprinid herpesvirus 2 (CyHV-2) propagation by activating signal transducer and activator of transcription 1 (STAT1), MX and protein kinase R (PKR) (52). Similarly, in EPC cells, CcMDA5 can reduce the expression level of the svcv-g gene and inhibit SVCV replication by inducing the expression of ifn-1, mx, viperin and isg15. MDA5-mediated antiviral signaling pathways are conserved in mammals and teleost.
LGP2, the third member of the RLR family, possesses the DExD/H box RNA helicase domain that is important for inducing IFN-b response (11). However, LGP2 lacks the CARD domain that is required for signaling, which is responsible for the controversial roles of LGP2 in RLRmediated signaling (53). To date, numerous pieces of evidence have illustrated the positive regulatory mechanism between the LGP2 and MDA5 (54). For instance, mammalian LGP2 can amplify the MDA5-mediated signaling pathway by augmenting the ability of MDA5 to form stable filaments (54, 55). In teleost, LGP2 plays a positive role in viral infection in Japanese flounder (53) and miiuy croaker (56). In line with these results, CcLGP2 overexpression significantly upregulated TBK1, IRF3, ifn-1, mx, viprein and isg15, and promoted MDA5-mediated ifn-1 activation. In contrast, LGP2, in mandarin fish (57), grouper (58) and rainbow trout (59) functions as a negative regulator in antiviral response by decreasing the transcription of IRF3, NF-kB and IFN-b. Recent studies have revealed that LGP2 is a bifunctional protein and yields a biphasic effect on MDA5 signaling (60,61). Hence, the role of fish LGP2 in antiviral response may be associated with fish and virus species.
Conclusively, the transcriptome profiling in the common carp gills, foregut and hindgut after SVCV infection was elaborated. Two key genes, CcMDA5 and CcLGP2, were identified post SVCV infection. CcLGP2 acted as a positive regulator in MDA5-induced ifn-1 activation. Collectively, the study will provide a better understanding for further investigation into the innate immune mechanism in common carp fight against viral infection.

Data availability statement
The data presented in the study are deposited in National Center for Biotechnology Information (NCBI) Short Read Archive (SRA) under accession number PRJNA873930. (https://www.ncbi. nlm.nih.gov/bioproject/PRJNA873930).

Ethics statement
The animal study was reviewed and approved by Animal Experimental Ethics Committee of Shandong Normal University.

Author contributions
SS and GY conceived and designed the experiments. RL, YN and performed the experiments and analyzed the data. YQ and HL helped with the experiments. SS and RL wrote the manuscript. All authors contributed to the article and approved the submitted version.


A clearing-free protocol for imaging intact whole adipose tissue innervation in mice

Summary Here we provide a clearing-free protocol for processing intact, whole mount subcutaneous white adipose tissue (scWAT) for immunofluorescence as an alternative to current clearing-based approaches. We use a combination of Z-depth reduction and autofluorescence quenching techniques to fluorescently label, image, and quantify adipose tissue innervation effectively throughout intact mouse tissues without the need for optical clearing or light sheet microscopy. This protocol has been optimized and validated for adipose neurovascular labeling. For complete details on the use and execution of this protocol, please refer to Willows et al. (2021).

MATERIALS AND EQUIPMENT
1. You will need access to a cold room (4 C) and an orbital shaker. 2. It is recommended to have an aspirator setup to remove buffers from wells between washes/incubations. 3. A pair of forceps and dissecting scissors. 4. Access to an epifluorescence and/or confocal microscope. In this step you will remove intact axillary and inguinal subcutaneous white adipose tissues from mice for immunostaining. For a demonstration of an axillary and inguinal subcutaneous white adipose tissue (scWAT) dissection see Methods video S1.
Note: This protocol is performed without perfusion. Any potential benefits or drawbacks of perfusion with this protocol have not been tested thoroughly at this time.
CRITICAL: Maintain inguinal scWAT orientation for all future steps, up to and including mounting. Most importantly, make note of whether the tissue was taken from mouseleft or mouse-right as the tissue will always curve towards the body when placed medial side up (congruent to tissue positioning during dissection, Figure 1). Additionally, the axillary end of the tissue will be wider than the posterior end. With these notes in mind, if it is known which side of the mouse the depot was excised from, it can always be returned to appropriate orientation when needed.
CRITICAL: Maintain axillary scWAT orientation for all future steps, up to and including mounting. The Axillary depot is more difficult to orientate than the inguinal depot as there are no obvious features for orientation. The most important step is to excise the tissue and place it medial side up into a well noting mouse-left or mouse-right. The axillary end of each tissue will have characteristic browning (in most cases) which can help orientate further, but this will be obscured with the addition of autofluorescence quenching stains described later.
2. Excise inguinal scWAT depot. a. Place mouse ventral side up, spray with 70% EtOH. b. Make a central incision from the base of the tail extending to the top of the sternum, being sure not to penetrate the underlying peritoneal cavity. c. Gently use forceps and thumbs to pull ventral skin away from both flanks, pulling slowly with even pressure, making sure not to damage the underlying adipose tissue. The skin can be pinned back (as demonstrated in Methods video S1) to better expose the adipose depots. d. Identify the inguinal scWAT depot against the skin besides each hip (Figure 1 -boundaries of each depot). Use scissors to cut the large vasculature leading to the depot and then trace the edges of the tissue, cutting along the way. e. Use forceps to gently roll the adipose away from the skin -cutting the anchoring connective tissue as you go. Continue until the entire depot is free from the skin. f. Weigh the depot and place it into a 6-well plate with 3-5 mL ice-cold 2% PFA and fix at 4 C. Fix the tissue without agitation to prevent the tissue from folding onto itself during the fixation process. i. %0.08g ; fix for 8 h.
Note: Once tissues are adequately fixed without agitation, they are unlikely to fold during incubations performed on an orbital shaker as utilized in later steps. 3. Excise axillary scWAT depot. a. Identify axillary depot underneath both forelimbs ( Figure 1). b. Use forceps to pull away a thin muscle layer enveloping the dorsolateral portion of each axillary depot. c. Use scissors to trace the edges of the tissue, cutting along the way. d. Use forceps to gently roll the adipose tissue away from the skin -cutting the anchoring connective tissue as you go. Continue until the entire depot is free from the skin. e. Weigh the depot and place it into a 6-well plate with 3-5 mL ice-cold 2% PFA to fix at 4 C. Fix the tissue without agitation. i. %0.08g; fix for 8 h.

Reducing tissue thickness and autofluorescence
Timing: Z-depth reduction, 1 h 30 min; permeabilization, 12 h; autofluorescence quenching, 4 h-1 day Adipose tissue is notoriously difficult to image due to its size and inherent autofluorescence. In this step we will be using a combination of Z-depth reduction and various autofluorescence quenching techniques to greatly reduce tissue background autofluorescence. It should be noted that hypertrophic adipocytes, and human adipocytes, may be more prone to cell lysis on the surface of the tissue with this technique. In some instances, whole tissue imaging is not required, and instead smaller Figure 1. Excising intact subcutaneous white adipose tissue (scWAT) depots from mice A male C57BL/6J mouse had skin pinned back to display inguinal and axillary scWAT depots before and after excision. White dashed lines are used to accentuate tissue boundaries. Tissues were kept medial side up (as in the intact scWAT image) and directionality was maintained; anterior (A, towards head) and posterior (P, towards tail).

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representative tissue pieces can be used. We have also outlined this alternative approach as an optional step below.
Note: All washes and incubations are performed on an orbital shaker.
a. Following fixation, aspirate PFA from each well of the 6-well plate and add 13 PBS w/heparin. Wash tissues for an hour at 4 C, replacing 13 PBS w/heparin after 30 min. Heparin is used here to help remove highly autofluorescent blood cells from the vasculature.
Pause point: The conclusion of any series of 13 PBS washes throughout this protocol marks a good pause point if desired (e.g. washes following fixation, autofluorescence quenching, or antibody staining.) Tissues can remain in 13 PBS for several days with no observed detriment to staining outcome. It is prudent to do antibody incubations immediately prior to imaging, for best results.
b. Use forceps to take the tissue out of PBS and lay it flat onto the center of a large (75 3 51 mm) glass slide. c. Use a transfer pipette to add 2-3 drops of PBS onto the tissue to prevent the tissue from drying out. d. Take a second large glass slide and place it on top of the tissue; sandwiching the tissue betwixt. e. Take two large (5 cm wide) binder clips and clamp them onto the slides ( Figure 2). f. Allow tissues to remain compressed for 1 h 30 min at 4 C. g. Remove binder clips and gently separate the two slides without damaging the tissue. See Optional: To entirely avoid compressing tissues, Z-depth reduction can be removed from this protocol by cutting each adipose tissue into 4-5 smaller pieces and following through with the remaining steps until mounting. Mount each piece of tissue onto a concave slide (0.8-1.9 mm deep, depending on tissue thickness). We do not recommend this alternative approach unless it is determined that tissue compression must be completely avoided. Only the most superficial structures will be visible. See Willows et al. (2021) for a comparison of both appropaches.
5. Blocking and Permeabilization. a. Once the slides are separated place the tissue back into a 6-well plate with 3-5 mL blocking buffer at 4 C for 16-24 h to reduce non-specific binding of antibodies.
Note: Here, bovine serum albumin is used as the blocking agent with great effect. Alternatively, using secondary-matched sera as a blocking agent is common with many other immunostaining protocols, but has not yet been thoroughly evaluated for use with this protocol. i. Pro -reduces autofluorescence without affecting fluorophore intensity. ii. Con -de/rehydration steps add several hours, and bleaching adds an additional day to the protocol. MeOH preparation has been demonstrated not to be compatible with some antibodies as it can denature epitopes (Renier et al., 2014) and should be tested and validated on a case by case basis. e. Option 4 -Room Temperature: doing all future steps at $20 C (even when recommended at 4 C) will reduce tissue autofluorescence, but only slightly. i. Pro -shortens protocol by at least 4 h while still offering some level of autofluorescence reduction. ii. Con -the least effective means of reducing autofluorescence and performing all future steps at $20 C can result in increased non-specific binding of antibodies.
Optional: Washes following autofluorescence quenching techniques can be performed in 13 PBS w/ heparin to further reduce vascular autofluorescence. Additionally, sodium azide can be added to PBS at a concentration of 0.02% for all future washes to prevent microbial growth.

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CRITICAL: The addition of non-ionic detergents (Triton X-100, Tween 20) into wash buffers is common to many immunostaining protocols, but if using typogen black or TrueBlack autofluorescence quenching, avoid the addition of these detergents as they can wash out these stains from the tissue, greatly reducing their quenching potential. The use of detergents has not been tested with the other quenching techniques.

Fluorescent labeling
Timing: 1-7 days The following steps outline how to fluorescently label Z-depth reduced adipose tissues or tissue blocks with primary antibodies, secondary antibodies, isolectin-IB 4 , and DAPI. It is important that, to reduce non-specific binding and cross reactivity, co-staining follows the order outlined below.
Note: All washes and incubations are performed on an orbital shaker.
7. Using a Direct Reporter with Endogenous Fluorescence a. For mouse reporters with high signal intensity you can proceed directly to step 12 or if co-labeling is desired, proceed to step 8. b. For mouse reporters with low signal intensity it is recommended to boost the endogenous signal with a fluorophore conjugated antibody raised against the fluorescent protein of interest (i.e., anti-GFP conjugated Alexa488). This should be conducted immediately after step 9 is completed. 8. Primary Antibodies. a. To make primary antibody solution, you will dilute desired primary antibody in 13 PBS. Dilutions are antibody specific but typically range from 1:100 to 1:500. For antibodies not validated in this protocol, we recommend performing an antibody titration to find what works best. When titrating antibodies for this protocol, it is best to start with what works well for thin sections as this can provide decent enough staining. However, concentrating this further by two-fold typically results in the best outcomes. Concentrations for antibodies that we have validated with this protocol can be found in the key resources table.
CRITICAL: Non-ionic detergents are commonly added to antibody solutions to increase antibody permeation and reduce non-specific binding, however, as mentioned previously, detergents can reduce the effectiveness of typogen black and TrueBlack, and has not been tested with the other quenching techniques.
b. You will need 3-5 mL of antibody solution per tissue, 3 mL being the absolute minimum if using a 6-well plate (see ''troubleshooting''.) c. Incubate tissues in primary antibody solution for 2 days at 4 C. d. After incubation, wash tissues in 13 PBS for 4 h at 4 C, replacing PBS every 1 h.
Note: Perform sequential staining of antibodies when co-staining.
Note: Only validated antibodies, tested in a Western Blot for specificity, and ideally with knock-out tissues and proper tissue controls, should be used for this procedure.
9. Secondary Antibodies a. If not using a directly conjugated primary antibody, dilute the desired secondary antibody in 13 PBS. Again, dilutions will be antibody and fluorophore specific, and should be optimized by titration. Secondary antibody concentrations tend to be identical to that used for immunostaining thin sections as recommended by the vendor. Only Alexa Fluor conjugated antibodies have been validated for this protocol. See validated secondary antibody concentrations in the key resources table. b. Incubate tissues in secondary antibody solution for 16-24 h at 4 C. c. After, wash tissues in 13 PBS for 4 h at 4 C, replacing PBS every 1 h. d. If co-staining with additional primary antibodies go back to step 8. If not, continue to step 10. CRITICAL: We recommend avoiding the use of anti-mouse secondary antibodies. In most cases, non-specific binding has been unavoidable even with mouse-on-mouse blocking solutions. We recommend using antibodies raised in mice that are directly conjugated to a fluorophore, or preferably, using primary antibodies raised in alternative species such as rabbits. a. If nuclear labeling is desired, start by making DAPI working solution from stock (described in materials and equipment), to be used fresh. b. Incubate tissues in 3-5 mL DAPI working solution for 1 h at $20 C. c. Wash in 13 PBS for 2 h at $20 C, replacing PBS after 1 h.

Mounting tissues
Timing: 3 days A benefit of our protocol is the ability to permanently mount entire subcutaneous adipose tissues onto glass slides. Below we outline the steps for mounting.
a. Label the top of a large glass slide with mouse and tissue identifying information. b. Remove tissue from 6-well plate and place it at the center of the large glass slide. c. Add a generous amount (4-6 drops) of glycerol based mounting fluid to the tissue and place a large (48 3 60 mm) glass coverslip over the tissue.
Note: Glycerol-based mountants are more compatible with lipid laden tissue, and the added viscosity aids in adhering the coverslip to the slide. Incomplete coverslip adherence will result in tissues drying out, tissue oxidation, and decreased fluorophore lifespan.
d. Apply gentle pressure to the coverslip to help it adhere to the tissue and slide. This is required for the next step to prevent the coverslip from sliding off the tissue. e. Place the coverslipped slides underneath uniform weight (15-20 kg) for 3 days. Ten textbooks placed over a series of slides works very well. f. After 3 days, remove the slide from underneath the textbooks and seal the edges with nail polish (Figure 4). g. Let dry for 5 min and proceed to imaging.
Note: For best outcome we recommend imaging within a week after mounting is complete. However, tissues are typically viable for imaging up to several weeks after mounting, and in some cases even several months.

Imaging
Timing: 1 min-40 h Unique to this tissue processing approach is the ability to image whole adipose tissues effectively on standard widefield epifluorescence microscopes as well as confocal microscopes. Below are descriptions for best utilizing both methods of imaging.
13. Widefield Epifluorescence Microscopy a. Application: i. Suitable for single channel imaging, most qualitative analysis, and answering 'yes or no' questions through representative images. Fast time to image with only minutes needed to set up and capture several images. b. Magnifications: i. Images of adipose tissue can be captured on widefield microscopes easily using up to 403 objectives. Higher magnifications are possible but the associated decrease in working distance will make it a challenge to capture an in-focus image of large tissues. i. Even with autofluorescence quenching, background will still be present on epifluorescence microscopes (see ''troubleshooting''). Strong staining and bright fluorophores can help overcome this, but confocal microscopy will further help to reduce background. 14. Confocal Microscopy a. Application: i. Suitable for quantification, demonstrating colocalization, and generating 3D images of specific regions or entire tissues to capture total neurovascular structure. b. Magnifications: i. Confocal microscopy is capable of imaging whole adipose tissues with up to 633 objectives without difficulty -further increased with confocal zoom. We have not tried higher objective magnifications at this time. c. Tiled Z-stacks: i. Entire adipose depots can be imaged in a series of Z-stacks extending through the entire tissue thickness. These can be tiled together to create one large 3D image. These can then be maximum intensity projected to create a 2D representation of the 3D data. These tiled z-stacks of whole tissues can be very time consuming to capture; taking anywhere from 8 to 40 h depending on the size of the tissue, number of fluorophores being captured, and the desired XY and Z resolutions.

EXPECTED OUTCOMES
With this protocol you will be able to fix and immunostain entire subcutaneous adipose depots and effectively image them without optical clearing on widefield and confocal microscopes. Below we

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have provided examples of images captured using this protocol ( Figure 5). For additional examples, see previous publications using this protocol (Blaszkiewicz et al., 2019(Blaszkiewicz et al., , 2020Willows et al., 2021).

QUANTIFICATION AND STATISTICAL ANALYSIS
Additionally, we have developed a means to quantify whole tissue innervation that is complementary to the tissue processing and imaging techniques described here. For further details see Willows et al. (2021) and access the code at https://github.com/ktownsendlab/willows_et_al-2020.

LIMITATIONS
This protocol was developed with adipose nerve and neurovascular immunostaining in mind. We strongly believe that it is applicable for any number of research goals but will need to be validated  for uses outside of the scope of this protocol. The Z-depth reduction approach can potentially alter tissue morphology, so we recommend researchers implementing this technique conduct their own observational analysis to ensure that Z-depth reduction does not create artefact in the structures they analyze. Z-depth reduction, by definition, compresses tissue in the Z-axis and results in reduced Z-axis spatial resolution. (Willows et al., 2021) provides comparisons of adipocyte size with and without this technique, for example.

TROUBLESHOOTING
Problem 1 Autofluorescence quenching with typogen black or TrueBlack reduces signal intensity and hides fluorescing structures.

Potential solution
Try using more dilute concentrations of these quenching agents as they can mask fluorescence signal. We have demonstrated the use of a few different concentrations (Willows et al., 2021). Alternatively, using hydrogen peroxide bleaching or negating autofluorescence quenching entirely may be preferable in certain circumstances.

Problem 2
Tissues don't compress sufficiently when coverslip is added during mounting.

Potential solution
Reduce fixation time. Overly fixed tissues will be a bit more rigid and tend to 'slide around' when gentle pressure is applied with the coverslip.

Problem 3
High background autofluorescence even after quenching.

Potential solution
The use of very bright fluorophores at high concentrations (anti-rabbit IgG Alex Fluor 647 Plus, 1:200) will overcome most background signal if imaged on a confocal microscope.

Problem 4
Low intensity fluorescence signal.

Potential solution
Increase the volume of antibody solutions to 5 mL and avoid conjugated primary antibodies if possible. Indirectly labeling with secondary antibodies will provide greater signal intensity. Additionally, increasing incubation times may also help.

Problem 5
Imaging entire whole depot innervation is taking 'too long.'

Potential solution
Adjust image acquisition settings. Optimizing image capture settings for a tiled Z max projection of an entire tissue can greatly reduce acquisition time. Settings will vary between confocal systems, but here are what we recommend for a Leica SP8 and Leica Stellaris 5 systems: Resolution 720 3 720 (can be reduced further if needed), scan speed 600 Hz, pinhole 1AU, scanned bi-directionally, 2line average, Z-step size of 10-16 mm, tiled with a 10% tile overlap. Increase laser intensity and gain as needed. DAPI and Alexa647 can be imaged simultaneously, but all other fluorophores must be imaged sequentially by line (or by frame if using DAPI; long depletion time can cause bleed through.) Using a microscope that can 'lasso' areas of interest for tiling instead of imaging entire rectangles will prevent wasting hours on imaging empty space.